Manifold Learning to Identify Consumer Profiles in Real Consumption Data

2019 ◽  
Author(s):  
Diego Perez ◽  
Marta Rivera-Alba ◽  
Alberto Sanchez-Carralero
Keyword(s):  
Manifold Learning ◽  
Consumption Data

The past, present and future of food consumption databases

2012 ◽  
Vol 153 (43) ◽  
pp. 1692-1700
Author(s):  
Viktória Szűcs ◽  
Erzsébet Szabó ◽  
Diána Bánáti
Keyword(s):  
Data Collection ◽  
Food Consumption ◽  
Age Groups ◽  
Collection Method ◽  
The Past ◽  
Consumption Data ◽  
Food Consumption Data ◽  
International Projects ◽  
Collection Methods ◽  
The Eu

Results of the food consumption surveys are utilized in many areas, such as for example risk assessment, cognition of consumer trends, health education and planning of prevention projects. Standardization of national consumption data for international comparison is an important task. The intention work began in the 1970s. Because of the widespread utilization of food consumption data, many international projects have been done with the aim of their harmonization. The present study shows data collection methods for groups of the food consumption data, their utilization, furthermore, the stations of the international harmonization works in details. The authors underline that for the application of the food consumption data on the international level, it is crucial to harmonize the surveys’ parameters (e.g. time of data collection, method, number of participants, number of the analysed days and the age groups). For this purpose the efforts of the EU menu project, started in 2012, are promising. Orv. Hetil., 2012, 153, 1692–1700.

A New Supervised Manifold Learning Algorithm Based on MMC and LSE

2014 ◽  
Vol 39 (12) ◽  
pp. 2077-2089
Author(s):  
Min YUAN ◽  
Lei CHENG ◽  
Ran-Gang ZHU ◽  
Ying-Ke LEI
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm

Generalized incremental manifold learning algorithm based on local smoothness

2013 ◽  
Vol 32 (6) ◽  
pp. 1670-1673
Author(s):  
Xue-yan ZHOU ◽  
Jian-min HAN ◽  
Yu-bin ZHAN
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Local Smoothness

3D facial depth map recognition based on manifold learning-LOGMAP algorithm

2013 ◽  
Vol 26 (2) ◽  
pp. 138-143 ◽  
Author(s):  
Shu Zhan ◽  
Zhihua Zhang ◽  
Changming Ye ◽  
Jianguo Jiang ◽  
S Ando
Keyword(s):  
Manifold Learning ◽  
Depth Map

Risk assessment of Campylobacter species in shellfish: identifying the unknown

1997 ◽  
Vol 35 (11-12) ◽  
pp. 29-34 ◽  
Author(s):  
P. Teunis ◽  
A. Havelaar ◽  
J. Vliegenthart ◽  
G. Roessink
Keyword(s):  
Risk Assessment ◽  
Quantitative Risk Assessment ◽  
Contamination Level ◽  
Risk Of Infection ◽  
Worst Case ◽  
Considerable Uncertainty ◽  
Risk Estimates ◽  
Consumption Data ◽  
Campylobacter Infection ◽  
Campylobacter Spp

Shellfish are frequently contaminated by Campylobacter spp, presumably originating from faeces from gulls feeding in the growing or relaying waters. The possible health effects of eating contaminated shellfish were estimated by quantitative risk assessment. A paucity of data was encountered necessitating many assumptions to complete the risk estimate. The level of Campylobacter spp in shellfish meat was calculated on the basis of a five-tube, single dilution MPN and was strongly season-dependent. The contamination level of mussels (<1/g) appeared to be higher than in oysters. The usual steaming process of mussels was found to completely inactivate Campylobacter spp so that risks are restricted to raw/undercooked shellfish. Consumption data were estimated on the basis of the usual size of a portion of raw shellfish and the weight of meat/individual animal. Using these data, season-dependent dose-distributions could be estimated. The dominant species in Dutch shellfish is C. lari but little is known on its infectivity for man. As a worst case assumption, it was assumed that the infectivity was similar to C. jejuni. A published dose-response model for Campylobacter-infection of volunteers is available but with considerable uncertainty in the low dose region. Using Monte Carlo simulation, risk estimates were constructed. The consumption of a single portion of raw shellfish resulted in a risk of infection of 5–20% for mussels (depending on season; 95% CI 0.01–60%). Repeated (e.g. monthly) exposures throughout a year resulted in an infection risk of 60% (95% CI 7–99%). Risks for oysters were slightly lower than for mussels. It can be concluded that, under the assumptions made, the risk of infection with Campylobacter spp by eating of raw shellfish is substantial. Quantitative risk estimates are highly demanding for the availability and quality of experimental data, and many research needs were identified.

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